Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making ...in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.
Cell death represents a basic biological paradigm that governs outcomes and long-term sequelae in almost every hepatic disease condition. Acute liver failure is characterized by massive loss of ...parenchymal cells but is usually followed by restitution ad integrum. By contrast, cell death in chronic liver diseases often occurs at a lesser extent but leads to long-term alterations in organ architecture and function, contributing to chronic hepatocyte turnover, the recruitment of immune cells and activation of hepatic stellate cells. These chronic cell death responses contribute to the development of liver fibrosis, cirrhosis and cancer. It has become evident that, besides apoptosis, necroptosis is a highly relevant form of programmed cell death in the liver. Differential activation of specific forms of programmed cell death might not only affect outcomes in liver diseases but also offer novel opportunities for therapeutic intervention. Here, we summarize the underlying molecular mechanisms and open questions about disease-specific activation and roles of programmed cell death forms, their contribution to response signatures and their detection. We focus on the role of apoptosis and necroptosis in acute liver injury, nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH) and liver cancer, and possible translations into clinical applications.
Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, ...because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer.
Hepatocellular death is present in almost all types of human liver disease and is used as a sensitive parameter for the detection of acute and chronic liver disease of viral, toxic, metabolic, or ...autoimmune origin. Clinical data and animal models suggest that hepatocyte death is the key trigger of liver disease progression, manifested by the subsequent development of inflammation, fibrosis, cirrhosis, and hepatocellular carcinoma. Modes of hepatocellular death differ substantially between liver diseases. Different modes of cell death such as apoptosis, necrosis, and necroptosis trigger specific cell death responses and promote progression of liver disease through distinct mechanisms. In this review, we first discuss molecular mechanisms by which different modes of cell death, damage-associated molecular patterns, and specific cell death responses contribute to the development of liver disease. We then review the clinical relevance of cell death, focusing on biomarkers; the contribution of cell death to drug-induced, viral, and fatty liver disease and liver cancer; and evidence for cell death pathways as therapeutic targets.
Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and the third-leading cause of cancer-related death worldwide, with incidence and mortality rates that are ...increasing. Recently, artificial intelligence (AI) has emerged as a unique opportunity to improve the full spectrum of HCC clinical care, by improving HCC risk prediction, diagnosis, and prognostication. AI approaches include computational search algorithms, machine learning (ML) and deep learning (DL) models. ML consists of a computer running repeated iterations of models, in order to progressively improve performance of a specific task, such as classifying an outcome. DL models are a subtype of ML, based on neural network structures that are inspired by the neuroanatomy of the human brain. A growing body of recent data now apply DL models to diverse data sources – including electronic health record data, imaging modalities, histopathology and molecular biomarkers – to improve the accuracy of HCC risk prediction, detection and prediction of treatment response. Despite the promise of these early results, future research is still needed to standardise AI data, and to improve both the generalisability and interpretability of results. If such challenges can be overcome, AI has the potential to profoundly change the way in which care is provided to patients with or at risk of HCC.
Circulating MicroRNAs as Biomarkers for Sepsis Benz, Fabian; Roy, Sanchari; Trautwein, Christian ...
International Journal of Molecular Sciences,
01/2016, Letnik:
17, Številka:
1
Journal Article, Book Review
Recenzirano
Odprti dostop
Sepsis represents a major cause of lethality during intensive care unit (ICU) treatment. Pharmacological treatment strategies for sepsis are still limited and mainly based on the early initiation of ...antibiotic and supportive treatment. In this context, numerous clinical and serum based markers have been evaluated for the diagnosis, the severity, and the etiology of sepsis. However until now, few of these factors could be translated into clinical use. MicroRNAs (miRNAs) do not encode for proteins but regulate gene expression by inhibiting the translation or transcription of their target mRNAs. Recently it was demonstrated that miRNAs are released into the circulation and that the spectrum of circulating miRNAs might be altered during various pathologic conditions, such as inflammation, infection, and sepsis. By using array- and single PCR-based methods, a variety of deregulated miRNAs, including miR-25, miR-133a, miR-146, miR-150, and miR-223, were described in the context of sepsis. Some of the miRNAs correlated with the disease stage, as well as patients' short and long term prognosis. Here, we summarize the current findings on the role of circulating miRNAs in the diagnosis and staging of sepsis in critically ill patients. We compare data from patients with findings from animal models and, finally, highlight the challenges and drawbacks that currently prevent the use of circulating miRNAs as biomarkers in clinical routine.
For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to ...objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images.
We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio HR with 95% confidence interval CI: 1.99 1.27-3.12, p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 1.14-2.33, p = 0.008), CRC-specific OS (HR 2.29 1.5-3.48, p = 0.0004), and relapse-free survival (RFS; HR 1.92 1.34-2.76, p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows.
In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.
Bone marrow-derived myeloid cells accumulate in the liver as monocytes and macrophages during the progression of obesity-related non-alcoholic fatty liver disease (NAFLD) to steatohepatitis (NASH). ...Myeloid cells comprise heterogeneous subsets, and dietary overnutrition may affect macrophages in the liver and bone marrow. We therefore aimed at characterising in depth the functional adaptations of myeloid cells in fatty liver.
We employed single-cell RNA sequencing to comprehensively assess the heterogeneity of myeloid cells in the liver and bone marrow during NAFLD, by analysing C57BL/6 mice fed with a high-fat, high-sugar, high-cholesterol 'Western diet' for 16 weeks. We also characterised NAFLD-driven functional adaptations of macrophages in vitro and their functional relevance during steatohepatitis in vivo.
Single-cell RNA sequencing identified distinct myeloid cell clusters in the liver and bone marrow. In both compartments, monocyte-derived populations were largely expanded in NASH-affected mice. Importantly, the liver myeloid compartment adapted a unique inflammatory phenotype during NAFLD progression, exemplarily characterised by downregulated inflammatory calprotectin (S100A8/A9) in macrophage and dendritic cell subsets. This distinctive gene signature was also found in their bone marrow precursors. The NASH myeloid phenotype was principally recapitulated by in vitro exposure of bone marrow-derived macrophages with fatty acids, depended on toll-like receptor 4 signalling and defined a characteristic response pattern to lipopolysaccharide stimulation. This imprinted and stable NASH myeloid immune phenotype functionally determined inflammatory responses following acute liver injury (acetaminophen poisoning) in vivo.
Liver myeloid leucocytes and their bone marrow precursors adapt a common and functionally relevant inflammatory signature during NAFLD progression.
Macrophages are key regulators of liver fibrosis progression and regression in nonalcoholic steatohepatitis (NASH). Liver macrophages comprise resident phagocytes, Kupffer cells, and monocyte‐derived ...cells, which are recruited through the chemokine receptor C‐C motif chemokine receptor 2 (CCR2). We aimed at elucidating the therapeutic effects of inhibiting monocyte infiltration in NASH models by using cenicriviroc (CVC), an oral dual chemokine receptor CCR2/CCR5 antagonist that is under clinical evaluation. Human liver tissues from NASH patients were analyzed for CCR2+ macrophages, and administration of CVC was tested in mouse models of steatohepatitis, liver fibrosis progression, and fibrosis regression. In human livers from 17 patients and 4 controls, CCR2+ macrophages increased parallel to NASH severity and fibrosis stage, with a concomitant inflammatory polarization of these cluster of differentiation 68+, portal monocyte‐derived macrophages (MoMF). Similar to human disease, we observed a massive increase of hepatic MoMF in experimental models of steatohepatitis and liver fibrosis. Therapeutic treatment with CVC significantly reduced the recruitment of hepatic Ly‐6C+ MoMF in all models. In experimental steatohepatitis with obesity, therapeutic CVC application significantly improved insulin resistance and hepatic triglyceride levels. In fibrotic steatohepatitis, CVC treatment ameliorated histological NASH activity and hepatic fibrosis. CVC inhibited the infiltration of Ly‐6C+ monocytes, without direct effects on macrophage polarization, hepatocyte fatty acid metabolism, or stellate cell activation. Importantly, CVC did not delay fibrosis resolution after injury cessation. RNA sequencing analysis revealed that MoMF, but not Kupffer cells, specifically up‐regulate multiple growth factors and cytokines associated with fibrosis progression, while Kupffer cells activated pathways related to inflammation initiation and lipid metabolism. Conclusion: Pharmacological inhibition of CCR2+ monocyte recruitment efficiently ameliorates insulin resistance, hepatic inflammation, and fibrosis, corroborating the therapeutic potential of CVC in patients with NASH. (Hepatology 2018;67:1270‐1283)
Both acute and chronic liver toxicity represents a major global health burden and an important cause of morbidity and lethality worldwide. Despite epochal progress in the treatment of hepatitis C ...virus infections, pharmacological treatment strategies for most liver diseases are still limited and new targets for prevention or treatment of liver disease are urgently needed. MicroRNAs (miRNAs) represent a new class of highly conserved small non-coding RNAs that are involved in the regulation of gene expression by targeting whole networks of so called "targets". Previous studies have shown that the expression of miRNAs is specifically altered in almost all acute and chronic liver diseases. In this context, it was shown that miRNA can exert causal roles, being pro- or anti-inflammatory, as well as pro- or antifibrotic mediators or being oncogenes as well as tumor suppressor genes. Recent data suggested a potential therapeutic use of miRNAs by targeting different steps in the hepatic pathophysiology. Here, we review the function of miRNAs in the context of acute and chronic liver diseases. Furthermore, we highlight the potential role of circulating microRNAs in diagnosis of liver diseases and discuss the major challenges and drawbacks that currently prevent the use of miRNAs in clinical routine.
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